Guiding Attention via a Cognitive Aid During a Simulated In-Hospital Cardiac Arrest Scenario: A Salience Effort Expectancy Value Model Analysis

OBJECTIVE

To investigate the effect of a cognitive aid on the visual attention distribution of the operator using the Salience Effort Expectancy Value (SEEV) model.

BACKGROUND

Cognitive aids aim to support an operator during the execution of a task. The effect of cognitive aids on performance is frequently evaluated but whether a cognitive aid improved, for example, attention distribution has not been considered.

METHOD

We built the Expectancy Value (EV) model version which can be considered to indicate optimal attention distribution for a given event. We analyzed the eye tracking data of emergency physicians while using a cognitive aid application versus no application during a simulated in-hospital cardiac arrest scenario.

RESULTS

The EV model could fit the attention distribution in such a simulated emergency situation. Partially supporting our hypothesis, the cognitive aid application group showed a significantly better EV model fit than the no application group in the first phases of the event, but a worse fit in the last phase.

CONCLUSION

We demonstrated that a cognitive aid affected attention distribution and that the SEEV model provides the means of capturing these effects. We suggest that the aid supported and improved visual attention distribution in the stressful first phases of a cardiopulmonary resuscitation but may have focused attention on objects that are relevant for lower priority goals in the last phase.

APPLICATION

The SEEV model can provide insights into expected and unexpected effects of cognitive aids on visual attention distribution and may help to design better artifacts.

Real-time tracking and documentation in trauma management

In trauma resuscitation, an accurate documentation is crucial to improve the quality of trauma care. Hospital emergency departments typically adopt handwritten paper records and flow sheets for acquiring data, which are often inaccurate. In this article, we describe TraumaTracker, a computer-based system for trauma tracking and documentation. Results demonstrate that completeness and accuracy of trauma documentation significantly improved using TraumaTracker, since it enables to add data and information that were not recorded in paper documentation - especially precise times and locations of events.